Similar to counterfactual explanations, semifactual explanations are an explanation type humans commonly use. They follow the pattern of Even if X, still P., which means that even if the input was changed in a certain way, the prediction of the model would still not change to the foil [1]}. In an XAI context, this means that an example, based on the original input, is provided that modifies the input in such a way that moves it toward the decision boundary of the model, but stops just before crossing it [2]}. Similar to counterfactual explanations, semifactual explanations can be used to guide a user’s future action, possibly in a way to deter them from moving toward the decision boundary [3]}.
